Understanding Concept Identification as Consistent Data Clustering Across Multiple Feature Spaces

TL;DR

This paper presents a novel approach to concept identification in large datasets, emphasizing consistency across multiple feature subsets, and demonstrates its advantages over traditional clustering methods in interpretability and decision support.

Contribution

It introduces a new perspective on concept identification as a specialized clustering task and proposes the use of mutual information to evaluate cluster consistency across feature subsets.

Findings

Identified clusters are more interpretable in energy management data.

Proposed method outperforms classical clustering in consistency across feature subsets.

Mutual information effectively measures cluster stability across features.

Abstract

Identifying meaningful concepts in large data sets can provide valuable insights into engineering design problems. Concept identification aims at identifying non-overlapping groups of design instances that are similar in a joint space of all features, but which are also similar when considering only subsets of features. These subsets usually comprise features that characterize a design with respect to one specific context, for example, constructive design parameters, performance values, or operation modes. It is desirable to evaluate the quality of design concepts by considering several of these feature subsets in isolation. In particular, meaningful concepts should not only identify dense, well separated groups of data instances, but also provide non-overlapping groups of data that persist when considering pre-defined feature subsets separately. In this work, we propose to view concept identification as a special form of clustering algorithm with a broad range of potential applications beyond engineering design. To illustrate the differences between concept identification and classical clustering algorithms, we apply a recently proposed concept identification algorithm to two synthetic data sets and show the differences in identified solutions. In addition, we introduce the mutual information measure as a metric to evaluate whether solutions return consistent clusters across relevant subsets. To support the novel understanding of concept identification, we consider a simulated data set from a decision-making problem in the energy management domain and show that the identified clusters are more interpretable with respect to relevant feature subsets than clusters found by common clustering algorithms and are thus more suitable to support a decision maker.